Ultrasensitive Biomolecule‐Less Nanosensor Based on β‐Cyclodextrin/Quinoline Decorated Graphene Oxide toward Prompt and Differentiable Detection of Corona and Influenza Viruses
Bibliographic record
Abstract
Abstract Rapid mutation of airborne pathogenic viruses, e.g., SARS‐CoV‐2, and their similar symptoms with flu or influenza, raises an urgent demand for the development of biomolecule‐less nanosensors capable of rapid, sensitive, specific, and differentiable detection of viruses in a single potential window to distinguish infected people from healthy ones through a precise and prompt manner that do not require highly purified biological receptors. To address this vital requirement, a label‐free, and biomolecule‐less nanosensor is designed and developed based on the modified graphene oxide (GO) with NHS/EDC activated β‐cyclodextrin/8 hydroxyquinoline (8HQ) complex toward rapid (in 1 min) and differentiable detection of betacoronaviruses (viz., SARS‐CoV‐2) and influenza viruses (viz., H1N1 and H3N2) in a single potential window. The outcome of the process shows that the employed process leads to considerable soar in the electrical conductivity, porosity, active surface area, available active sites for trapping viruses, and sensitivity of the nanosensor that leads to rapid, sensitive, specific, and simultaneous detection of selected pathogenic viruses with a superiorly low detection limit (DL) and high sensitivity. Obtained results highlight the potential of the developed nanoplatform as a capable screening tool for quick detection of infected people.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".